Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study
Open accessAbstract
BACKGROUND: Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases. RESULTS: Ada DX suggested the correct disease earlier than the time of clinical diagnosis among the top five fit disease suggestions in 53.8% of cases (50 of 93), and as the top fit disease suggestion in 37.6% of cases (35 of 93). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. Wilcoxon signed-rank test shows a significant difference between the time to clinical diagnosis and the time to correct disease suggestion for both top five fit and top fit (z-score -6.68, respective -5.71, α=0.05, p-value <0.001). CONCLUSION: Ada DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases to physicians early in the course of a case. Limitations of this study derive from its retrospective and unblinded design, data input by a single user, and the optimization of the knowledge base during the course of the study. Results pertaining to the system's accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies.
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A retrospective study with a focus on rare diseases demonstrated that Ada suggested accurate diagnoses earlier than clinical diagnoses in more than half of all cases.
“The DDSS prototype has been examined in a retrospective study with a focus on rare diseases, demonstrating that Ada suggested accurate diagnoses earlier than clinical diagnoses in more than half of all cases [12].”
From A Novel Diagnostic Decision Support System for Medical Professionals: Prospective Feasibility Study (2022) · cites this paper as [12]
The acceleration of rare disease diagnosis and the higher accuracy in the inpatient setting indicate that, in its current form, the DDSS is more suited to those disciplines.
“The acceleration of rare disease diagnosis [12] and the higher accuracy in the inpatient setting also indicate that, in its current form, the DDSS is more suited to those disciplines.”
From A Novel Diagnostic Decision Support System for Medical Professionals: Prospective Feasibility Study (2022) · cites this paper as [12]
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A Novel Diagnostic Decision Support System for Medical Professionals: Prospective Feasibility Study
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Self-citation
- Authors
- Ronicke, S., Hirsch, M. C., Türk, E., Larionov, K., Tientcheu, D., & Wagner, A. D.
- Affiliations
- Medizinische Hochschule Hannover (DE)
- Corresponding author
- Ronicke, Simon
- Venue
- Orphanet Journal of Rare Diseases
- Published
- 2019-03-21
- Volume
- 14
- Issue
- 1
- Pages
- 69-69
- ISSN
- 1750-1172 (ISSN-L)
- PMC
- PMC6427854
- OpenAlex
- W2943735103
- Cited by
- 134
- References
- 30
- Citations by year
- 2026: 13 · 2025: 12 · 2024: 18 · 2023: 13 · 2022: 24 · 2021: 34 · 2020: 15 · 2019: 5
- FWCI
- 6.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Orphanet Journal of Rare Diseases (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- rare diseases, diagnostic decision support systems, differential diagnosis, time to diagnosis, early diagnosis, probabilistic diagnosis, knowledge base, clinical decision support
- MeSH terms
- Adult, Female, Humans, Male, Middle Aged, Retrospective Studies, Software, Time Factors, Reproducibility of Results, Decision Support Systems, Clinical, Rare Diseases
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